This invention relates to a method and apparatus for segmenting an image using a combination of image segmentation techniques. More particularly, the invention is directed to an improved image segmentation technique for use in an image processing system that performs at least two distinct image segmentation processes on an image and combines the results to obtain a combined multi-layer representation of the image that can be suitably processed. In a specific example, a block based segmentation technique is performed on an image to generate a MRC (mixed raster content) representation—having foreground, background and selector layers. A pixel based segmentation technique is also performed on the image to generate rendering hints. The MRC representation and the rendering hints are then combined to obtain a four (4) layer representation of the image. The four layer representation is subsequently processed as required by the image processing system, e.g. compressed and stored.
While the invention is particularly directed to the art of combining image segmentation techniques to obtain a useful result, and will be thus described with specific reference thereto, it will be appreciated that the invention may have usefulness in other fields and applications.
By way of background, various methods for segmenting images are known. In general, such image segmentation methods are implemented to satisfy a wide variety of image processing needs. For example, when an image is sought to be compressed, it is advantageous to first determine the types of objects (e.g. continuous tone objects, background portions, text, . . . etc.) that are contained in the image. Compression techniques, depending on their precise nature, tend to most effectively compress only certain types of image objects. Thus, images are segmented by object type so that appropriate compression techniques may be applied to each of the respective object types of the image. To illustrate, it is well known in the image processing field that JPEG compression techniques work fairly well on continuous tone pixel maps but do not operate effectively on text. Conversely, the Lempel-Ziv Welch compression techniques do not perform adequately on scanned pixel maps.
Moreover, the various types of image segmentation methods each possess relative strengths. For example, pixel based image segmentation methods allow for improved image rendering capabilities over other segmentation methods. In this regard, pixel level segmentation methods generate pixel level rendering hints—which are pieces of information that indicate certain characteristics about an image. For example, a rendering hint may indicate the location of an edge within an image. Corresponding windows (whereby all pixels within a window have the same rendering hints) are also utilized. Although the generation of rendering hints and categorization using window identifications are advantageous features of pixel level segmentation from the standpoint of image rendering, a severe disadvantage of such methods is that compression ratios of a pixel based segmented image are not acceptable for many applications.
Other image segmentation methods that are well known are referred to as block based segmentation methods. That is, the subject image is segmented on a block-by-block basis as opposed to a pixel-by-pixel basis. Block based image segmentation methods attain improved compression ratios over pixel based methods and also are conducive to generating mixed raster content (MRC) data for ease of compression. The disadvantage of block based image segmentation methods, however, is that rendering hints are not effectively generated using these methods. Even if they are generated, use thereof tends to place artifacts on the rendered image.
As such, a segmentation system that combines the advantages of the above referenced segmentation methods, and others, and utilizes such advantages for improved rendering is desired.
The present invention contemplates a new and improved image segmentation method and apparatus that resolves the above-referenced difficulties and others.